core-sdk

分类: 开发与编程 | 上传者: terrene-foundation | 下载: 0 | 版本: v1.0（最新）

Kailash Core SDK 基础知识包括工作流创建、节点模式、连接、运行时执行、参数传递、错误处理、循环工作流、异步模式、MCP 集成和安装。在询问“工作流基础知识”、“核心 sdk”、“创建工作流”、“工作流生成器”、“节点模式”、“连接”、“运行时”、“参数”、“导入”、“安装”、“入门”、“工作流执行”、“异步工作流”、“错误处理”、“循环工作流”、“PythonCode 节点”时使用“SwitchNode”或“MCP 集成”。还涵盖可观察性：“OpenTelemetry”、“OTel”、“tracing”、“TracingLevel”、“WorkflowTracer”、“节点检测”、“NodeInstrumentor”、“数据库检测”、“Prometheus 指标”、“MetricsBridge”、“KAILASH_TRACING_LEVEL”或“span 属性”。

更新日志: Source: GitHub https://github.com/terrene-foundation/kailash-coc-claude-py

目录结构

当前层级: tree/main/.claude/skills/01-core-sdk/

📄 async-pythoncode-patterns.md 6.8 KB
📄 async-resource-safety.md 10.7 KB
📄 async-workflow-patterns.md 522 B
📄 connection-patterns.md 9.0 KB
📄 core-workflow-dag.md 1.4 KB
📄 custom-node-guide.md 701 B
📄 cycle-workflows-basics.md 3.6 KB
📄 error-handling-patterns.md 534 B
📄 kailash-imports.md 8.1 KB
📄 kailash-installation.md 7.0 KB
📄 mcp-integration-guide.md 403 B
📄 node-patterns-common.md 9.4 KB
📄 otel-tracing.md 2.2 KB
📄 param-passing-quick.md 5.9 KB
📄 production-readiness-patterns.md 9.5 KB
📄 pythoncode-best-practices.md 511 B
📄 runtime-execution.md 11.6 KB
📄 runtime-lifecycle.md 5.1 KB
📄 SKILL.md 7.9 KB
📄 switchnode-patterns.md 4.1 KB
📄 workflow-quickstart.md 12.0 KB

SKILL.md

---
name: core-sdk
description: "Kailash Core SDK fundamentals including workflow creation, node patterns, connections, runtime execution, parameter passing, error handling, cyclic workflows, async patterns, MCP integration, and installation. Use when asking about 'workflow basics', 'core sdk', 'create workflow', 'workflow builder', 'node patterns', 'connections', 'runtime', 'parameters', 'imports', 'installation', 'getting started', 'workflow execution', 'async workflows', 'error handling', 'cyclic workflows', 'PythonCode node', 'SwitchNode', or 'MCP integration'. Also covers observability: 'OpenTelemetry', 'OTel', 'tracing', 'TracingLevel', 'WorkflowTracer', 'node instrumentation', 'NodeInstrumentor', 'database instrumentation', 'Prometheus metrics', 'MetricsBridge', 'KAILASH_TRACING_LEVEL', or 'span attributes'."
---

# Kailash Core SDK - Foundational Skills

Comprehensive guide to Kailash Core SDK fundamentals for workflow automation and integration.

## Features

The Core SDK provides the foundational building blocks for creating custom workflows with fine-grained control:

- **110+ Workflow Nodes**: Pre-built nodes for AI, API, database, file operations, logic, and more
- **WorkflowBuilder API**: String-based workflow construction with type safety
- **Dual Runtime Support**: AsyncLocalRuntime (Docker/async) and LocalRuntime (CLI/scripts)
- **Advanced Patterns**: Cyclic workflows, conditional execution, error handling
- **MCP Integration**: Built-in Model Context Protocol support
- **Parameter Passing**: Flexible data flow between nodes
- **Zero Configuration**: Auto-detection of runtime context
- **Production Ready**: Enterprise features including monitoring, validation, and debugging

## Quick Start

```python
from kailash.workflow.builder import WorkflowBuilder
from kailash.runtime.local import LocalRuntime

workflow = WorkflowBuilder()
workflow.add_node("NodeName", "id", {"param": "value"})

# Use context manager for proper resource cleanup (recommended)
with LocalRuntime() as runtime:
    results, run_id = runtime.execute(workflow.build())
```

## Reference Documentation

### Getting Started

- **[workflow-quickstart](workflow-quickstart.md)** - Create basic workflows with WorkflowBuilder
- **[kailash-installation](kailash-installation.md)** - Installation and setup guide
- **[kailash-imports](kailash-imports.md)** - Import patterns and module organization

### Core Patterns

- **[node-patterns-common](node-patterns-common.md)** - Common node usage patterns
- **[connection-patterns](connection-patterns.md)** - Linking nodes and data flow
- **[param-passing-quick](param-passing-quick.md)** - Parameter passing strategies
- **[runtime-execution](runtime-execution.md)** - Executing workflows (sync/async)
- **[runtime-lifecycle](runtime-lifecycle.md)** - Runtime lifecycle, ref counting, acquire/release, context managers

### Advanced Topics

- **[async-workflow-patterns](async-workflow-patterns.md)** - Asynchronous workflow execution
- **[async-resource-safety](async-resource-safety.md)** - `__del__` hardening, double-check locking, pool lifecycle, static analysis guardrails
- **[cycle-workflows-basics](cycle-workflows-basics.md)** - Cyclic workflow patterns
- **[error-handling-patterns](error-handling-patterns.md)** - Error management strategies
- **[switchnode-patterns](switchnode-patterns.md)** - Conditional routing with SwitchNode
- **[pythoncode-best-practices](pythoncode-best-practices.md)** - PythonCode node best practices
- **[mcp-integration-guide](mcp-integration-guide.md)** - Model Context Protocol integration

## Key Concepts

### Canonical Node Pattern (4-Parameter)

**This is the single source of truth for node configuration.** All other skills reference this section.

```python
workflow.add_node(
    "NodeClassName",  # 1. Node type (PascalCase, string)
    "unique_node_id", # 2. Unique ID (snake_case, string)
    {                 # 3. Configuration dict
        "param1": "value",
        "param2": 123
    },
    connections=[]    # 4. Optional: input connections
)
```

| Parameter   | Type | Description                          | Example                         |
| ----------- | ---- | ------------------------------------ | ------------------------------- |
| Node type   | str  | The node class name (PascalCase)     | `"LLMNode"`, `"HTTPRequest"`    |
| Node ID     | str  | Unique identifier (snake_case)       | `"fetch_data"`, `"process_1"`   |
| Config      | dict | Node-specific configuration          | `{"url": "..."}`                |
| Connections | list | Optional input connections (4-tuple) | `[("src", "out", "dst", "in")]` |

**Connection Methods**:

```python
# Method 1: add_connection (4-positional params - explicit)
workflow.add_connection("read_file", "content", "transform", "input")

# Method 2: connect (flexible API with keyword args)
workflow.connect("read_file", "transform", from_output="content", to_input="input")

# Method 3: connect with mapping (multiple outputs)
workflow.connect("node1", "node2", mapping={"content": "input", "meta": "metadata"})
```

### WorkflowBuilder Pattern

- String-based node API: `workflow.add_node("NodeName", "id", {})`
- Always call `.build()` before execution
- Never `workflow.execute(runtime)` - always `runtime.execute(workflow.build())`

### Runtime Selection

- **AsyncLocalRuntime**: For Docker/async (async contexts) - async-first, no threading, 10-100x faster
- **LocalRuntime**: For CLI/scripts (sync contexts) - synchronous execution with thread support
- **get_runtime()**: Auto-detection helper that selects appropriate runtime based on context

Both runtimes return identical structure: `(results, run_id)` tuple.

### Runtime Architecture

Both LocalRuntime and AsyncLocalRuntime inherit from BaseRuntime with shared capabilities:

**BaseRuntime Foundation**:

- 29 configuration parameters (debug, enable_cycles, conditional_execution, connection_validation, etc.)
- Execution metadata management
- Common initialization and validation modes (strict, warn, off)

**Shared Mixins**:

- **CycleExecutionMixin**: Cyclic workflow execution with validation
- **ValidationMixin**: Workflow structure validation (5 methods)
- **ConditionalExecutionMixin**: Conditional execution and branching with SwitchNode support

**AsyncLocalRuntime-Specific**:

- WorkflowAnalyzer for optimal execution strategy
- Level-based parallelism for concurrent execution
- Thread pool for sync nodes without blocking
- Semaphore control to prevent resource exhaustion

## Critical Rules

- ✅ ALWAYS: `runtime.execute(workflow.build())`
- ✅ String-based nodes: `workflow.add_node("NodeName", "id", {})`
- ✅ 4-parameter connections: `(source_id, source_param, target_id, target_param)`
- ✅ Docker/async: Use AsyncLocalRuntime (mandatory)
- ✅ CLI/Scripts: Use LocalRuntime
- ❌ NEVER: `workflow.execute(runtime)`
- ❌ NEVER: Instance-based nodes
- ❌ NEVER: Use LocalRuntime in Docker (causes hangs)

## When to Use This Skill

Use this skill when you need to:

- Create custom workflows from scratch
- Understand workflow fundamentals
- Learn node patterns and connections
- Set up runtime execution
- Handle errors in workflows
- Implement cyclic or async patterns
- Integrate with MCP
- Get started with Kailash SDK

## Related Skills

- **[02-dataflow](../02-dataflow/SKILL.md)** - Database operations framework built on Core SDK
- **[03-nexus](../03-nexus/SKILL.md)** - Multi-channel platform framework built on Core SDK
- **[04-kaizen](../04-kaizen/SKILL.md)** - AI agent framework built on Core SDK
- **[06-cheatsheets](../06-cheatsheets/SKILL.md)** - Quick reference patterns
- **[08-nodes-reference](../08-nodes-reference/SKILL.md)** - Complete node reference
- **[09-workflow-patterns](../09-workflow-patterns/SKILL.md)** - Industry workflow templates
- **[17-gold-standards](../17-gold-standards/SKILL.md)** - Mandatory best practices

## Support

For complex workflows or debugging, invoke:

- `pattern-expert` - Workflow patterns and cyclic debugging
- `testing-specialist` - Test workflow implementations

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